{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pypercolate HPC performance metrics"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In this Section, we compute basic performance metrics of the *percolate.hpc* module.\n",
    "Namely, we time the execution and measure memory consumption of graph creation with :meth:`~percolate.percolate.spanning_2d_grid`, of computing the convolution coefficients with :meth:`~percolate.percolate._binomial_pmf`, and of performing a single run with :meth:`~percolate.hpc.bond_microcanonical_statistics`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# configure plotting\n",
    "%config InlineBackend.rc = {'figure.dpi': 300, 'savefig.dpi': 300, \\\n",
    "                            'figure.figsize': (6, 3), 'font.size': 12, \\\n",
    "                            'figure.facecolor': (1, 1, 1, 0)}\n",
    "%matplotlib inline\n",
    "\n",
    "import timeit\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import memory_profiler\n",
    "import numpy as np\n",
    "\n",
    "import percolate\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## System sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We determine performance metrics for the following system sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   8   16   32   64  128  256  512 1024]\n"
     ]
    }
   ],
   "source": [
    "dimensions = 2 ** np.arange(3, 11)\n",
    "np.save('dimensions.npy', dimensions)\n",
    "print(dimensions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## System information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Section details the hardware, the operating system, and the Python environment that collects the performance metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arch': 'X86_64',\n",
       " 'brand': 'Intel(R) Core(TM) i7-2620M CPU @ 2.70GHz',\n",
       " 'count': 4,\n",
       " 'hz_advertised': '2.7000 GHz',\n",
       " 'l2_cache_size': '4096 KB',\n",
       " 'vendor_id': 'GenuineIntel'}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# $ pip install py-cpuinfo\n",
    "from cpuinfo import cpuinfo\n",
    "{\n",
    "    key: value\n",
    "    for key, value in cpuinfo.get_cpu_info().items()\n",
    "    if key in ['arch', 'brand', 'count', 'hz_advertised', 'l2_cache_size', 'vendor_id']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total memory: 7.7 GiB\n"
     ]
    }
   ],
   "source": [
    "import psutil\n",
    "print(\"Total memory: {:.1f} GiB\".format(psutil.virtual_memory().total / 1024 ** 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import platform\n",
    "import sys\n",
    "\n",
    "import pip\n",
    "import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Linux-3.13.0-24-generic-x86_64-with-LinuxMint-17-qiana'\n"
     ]
    }
   ],
   "source": [
    "print(platform.platform())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.4.0 CPython GCC 4.8.2\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    platform.python_version(),\n",
    "    platform.python_implementation(),\n",
    "    platform.python_compiler(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cython': '0.22.1',\n",
      " 'future': '0.15.0',\n",
      " 'ipython': '3.2.1',\n",
      " 'matplotlib': '1.4.3',\n",
      " 'memory-profiler': '0.33',\n",
      " 'networkx': '1.10',\n",
      " 'numpy': '1.9.2',\n",
      " 'percolate': '0.3.0.post0.dev10+gce92429.dirty',\n",
      " 'pip': '7.1.0',\n",
      " 'scipy': '0.16.0',\n",
      " 'simoa': '0.4.1'}\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint({\n",
    "    package.key: package.version\n",
    "    for package in pip.get_installed_distributions()\n",
    "    if package.key in [\n",
    "        'cython', 'future', 'ipython', 'matplotlib', 'memory-profiler',\n",
    "        'networkx', 'numpy', 'percolate', 'pip', 'scipy', 'simoa',\n",
    "    ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "openblas_info:\n",
      "    library_dirs = ['/usr/lib']\n",
      "    language = f77\n",
      "    libraries = ['openblas']\n",
      "blas_mkl_info:\n",
      "  NOT AVAILABLE\n",
      "mkl_info:\n",
      "  NOT AVAILABLE\n",
      "openblas_lapack_info:\n",
      "  NOT AVAILABLE\n",
      "lapack_opt_info:\n",
      "    include_dirs = ['/usr/include/atlas']\n",
      "    library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base']\n",
      "    language = f77\n",
      "    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']\n",
      "    define_macros = [('ATLAS_INFO', '\"\\\\\"3.10.1\\\\\"\"')]\n",
      "lapack_mkl_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_3_10_threads_info:\n",
      "  NOT AVAILABLE\n",
      "blas_opt_info:\n",
      "    library_dirs = ['/usr/lib']\n",
      "    language = f77\n",
      "    libraries = ['openblas']\n",
      "atlas_info:\n",
      "    include_dirs = ['/usr/include/atlas']\n",
      "    library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base']\n",
      "    language = f77\n",
      "    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']\n",
      "    define_macros = [('ATLAS_INFO', '\"\\\\\"3.10.1\\\\\"\"')]\n",
      "atlas_threads_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_3_10_info:\n",
      "  NOT AVAILABLE\n"
     ]
    }
   ],
   "source": [
    "np.show_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each performance metric, we define and run a script in an independent process.\n",
    "Each script reads the system sizes from disk and writes the performance metrics back to disk.\n",
    "We use independent scripts and processes here as especially memory usage measurements seem to be volatile if executed in the same process, IPython session, or notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%python\n",
    "\n",
    "import memory_profiler\n",
    "import numpy as np\n",
    "import percolate\n",
    "\n",
    "def get_graph_memory(dimension):\n",
    "    mem_before = memory_profiler._get_memory(-1)\n",
    "    graph = percolate.spanning_2d_grid(dimension)\n",
    "    mem_after = memory_profiler._get_memory(-1)\n",
    "    return mem_after - mem_before\n",
    "\n",
    "dimensions = np.load('dimensions.npy')\n",
    "graph_mem = np.fromiter(\n",
    "    (\n",
    "        get_graph_memory(dimension)\n",
    "        for dimension in dimensions\n",
    "    ),\n",
    "    dtype=np.float,\n",
    ")\n",
    "np.save('graph_mem.npy', graph_mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%python\n",
    "\n",
    "import memory_profiler\n",
    "import numpy as np\n",
    "import percolate\n",
    "\n",
    "def get_convolution_memory(dimension):\n",
    "    mem_before = memory_profiler._get_memory(-1)\n",
    "    convolution_factors = percolate.percolate._binomial_pmf(\n",
    "        n=2 * dimension * (dimension - 1), p=0.5,\n",
    "    )\n",
    "    mem_after = memory_profiler._get_memory(-1)\n",
    "    return mem_after - mem_before\n",
    "\n",
    "dimensions = np.load('dimensions.npy')\n",
    "convolution_mem = np.fromiter(\n",
    "    (\n",
    "        get_convolution_memory(dimension)\n",
    "        for dimension in dimensions\n",
    "    ),\n",
    "    dtype=np.float,\n",
    ")\n",
    "np.save('convolution_mem.npy', convolution_mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%python\n",
    "\n",
    "import timeit\n",
    "\n",
    "import numpy as np\n",
    "import percolate\n",
    "\n",
    "dimensions = np.load('dimensions.npy')\n",
    "graph_times = np.fromiter(\n",
    "    (\n",
    "        timeit.timeit(\n",
    "            stmt='percolate.spanning_2d_grid({})'.format(dimension),\n",
    "            setup='import percolate',\n",
    "            number=1,\n",
    "        )\n",
    "        for dimension in dimensions\n",
    "    ),\n",
    "    dtype=np.float,\n",
    ")\n",
    "np.save('graph_times.npy', graph_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%python\n",
    "\n",
    "import timeit\n",
    "\n",
    "import numpy as np\n",
    "import percolate\n",
    "\n",
    "dimensions = np.load('dimensions.npy')\n",
    "\n",
    "convolution_stmt = \"\"\"\\\n",
    "[\n",
    "    percolate.percolate._binomial_pmf(n={}, p=p)\n",
    "    for p in np.linspace(0.4, 0.6, num=1)\n",
    "]\n",
    "\"\"\"\n",
    "\n",
    "convolution_times = np.fromiter(\n",
    "    (\n",
    "        timeit.timeit(\n",
    "            stmt=convolution_stmt.format(2 * dimension * (dimension - 1)),\n",
    "            setup='import percolate; import numpy as np',\n",
    "            number=1,\n",
    "        )\n",
    "        for dimension in dimensions\n",
    "    ),\n",
    "    dtype=np.float,\n",
    ")\n",
    "np.save('convolution_times.npy', convolution_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:302: RuntimeWarning: invalid value encountered in power\n",
      "  ret['moments'] += ret['max_cluster_size'] ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:286: RuntimeWarning: invalid value encountered in power\n",
      "  ret['moments'] -= weights[i] ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:286: RuntimeWarning: invalid value encountered in subtract\n",
      "  ret['moments'] -= weights[i] ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:297: RuntimeWarning: invalid value encountered in power\n",
      "  ret['moments'] += weight ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:297: RuntimeWarning: invalid value encountered in add\n",
      "  ret['moments'] += weight ** np.arange(5)\n"
     ]
    }
   ],
   "source": [
    "%%python\n",
    "\n",
    "import timeit\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "dimensions = np.load('dimensions.npy')\n",
    "\n",
    "run_stmt = \"\"\"\\\n",
    "percolate.hpc.bond_microcanonical_statistics(\n",
    "    seed=42, **perc_graph\n",
    ")\n",
    "\"\"\"\n",
    "run_setup = \"\"\"\\\n",
    "import percolate\n",
    "import percolate.hpc\n",
    "\n",
    "perc_graph = percolate.percolate.percolation_graph(\n",
    "    graph=percolate.spanning_2d_grid({}),\n",
    "    spanning_cluster=True,\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "run_times = np.fromiter(\n",
    "    (\n",
    "        timeit.timeit(\n",
    "            stmt=run_stmt,\n",
    "            setup=run_setup.format(dimension),\n",
    "            number=1,\n",
    "        )\n",
    "        for dimension in dimensions\n",
    "    ),\n",
    "    dtype=np.float,\n",
    ")\n",
    "np.save('run_times.npy', run_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:302: RuntimeWarning: invalid value encountered in power\n",
      "  ret['moments'] += ret['max_cluster_size'] ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:286: RuntimeWarning: invalid value encountered in power\n",
      "  ret['moments'] -= weights[i] ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:286: RuntimeWarning: invalid value encountered in subtract\n",
      "  ret['moments'] -= weights[i] ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:297: RuntimeWarning: invalid value encountered in power\n",
      "  ret['moments'] += weight ** np.arange(5)\n",
      "/home/sorge/repos/sci/pypercolate/percolate/hpc.py:297: RuntimeWarning: invalid value encountered in add\n",
      "  ret['moments'] += weight ** np.arange(5)\n"
     ]
    }
   ],
   "source": [
    "%%python\n",
    "\n",
    "import memory_profiler\n",
    "import numpy as np\n",
    "import percolate\n",
    "import percolate.hpc\n",
    "\n",
    "def get_run_memory(dimension):\n",
    "    perc_graph = percolate.percolate.percolation_graph(\n",
    "        graph=percolate.spanning_2d_grid(dimension),\n",
    "        spanning_cluster=True,\n",
    "    )\n",
    "    mem_before = memory_profiler._get_memory(-1)\n",
    "    return (\n",
    "        max(memory_profiler.memory_usage(\n",
    "            (\n",
    "                percolate.hpc.bond_microcanonical_statistics,\n",
    "                [],\n",
    "                dict(seed=42, **perc_graph)\n",
    "            ),\n",
    "            interval=.01,\n",
    "        )) - mem_before\n",
    "    )\n",
    "\n",
    "dimensions = np.load('dimensions.npy')\n",
    "run_mem = np.fromiter(\n",
    "    (\n",
    "        get_run_memory(dimension)\n",
    "        for dimension in dimensions\n",
    "    ),\n",
    "    dtype=np.float,\n",
    ")\n",
    "np.save('run_mem.npy', run_mem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "graph_mem = np.load('graph_mem.npy')\n",
    "convolution_mem = np.load('convolution_mem.npy')\n",
    "graph_times = np.load('graph_times.npy')\n",
    "convolution_times = np.load('convolution_times.npy')\n",
    "run_times = np.load('run_times.npy')\n",
    "run_mem = np.load('run_mem.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b4aeb02b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(dimensions ** 2, graph_times, 'x')\n",
    "plt.xlabel(r'number of nodes')\n",
    "plt.ylabel(r'execution time (s)')\n",
    "plt.title(r'percolate.spanning\\_2d\\_grid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b4ac7eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(dimensions ** 2, graph_mem / 2 ** 10, 'x')\n",
    "plt.xlabel(r'number of nodes')\n",
    "plt.ylabel('memory consumption (GiB)')\n",
    "plt.title('percolate.spanning\\_2d\\_grid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dGLGj1//nwsLCzE4uJiLC4ubtgumUzGxMREnDt3bocthsHRq7+h\niUQi7t27F9PT07G4uBhLS0uNtYhWVlbi2LFjfgAFHehFjVar1fj0008b19dPBdnPhEOwS1v9h3cr\nnXRKffzxxzt6bGDNXtfn+Pi4hXdhD/Xib+jo6GiMjo42flVZLpcjmUxGOp2OU6dO7bSpMHB6+R23\nzndd2Lle1GgqlYqHDx821u+rVquRTCbjxIkTpmGFLvTyb2gikYi33347KpVKrKysNOr0Jz/5iTqF\nDvWiRpPJZDx8+DASiUTUarVD82NE4RDsoW4+XJrT5m6SaqB76hP6217X6MjISIyMjOy6XYC/odDv\n9rpG6yMRgN3r1d/QVCplJB/sgb2s0XogdFiCoYiI7xx0A4A1h+mDAwaN+oT+pkahf6lP6G9qFPqX\n+oT+dhRqVDgEu7Q+JV4/Z+V2djqEEeic+oT+pkahf6lP6G9qFPqX+oT+pka/JRyCXUomk43L3XxI\n1Gq1DddTqdSetQlYoz6hv6lR6F/qE/qbGoX+pT6hv6nRbwmHYJf2Im1e/6EE7B31Cf1NjUL/Up/Q\n39Qo9C/1Cf1NjX5LOAS79IMf/GBH+63/8Emn03vVHGAd9Qn9TY1C/1Kf0N/UKPQv9Qn9TY1+SzgE\nu9Q8hLB5iGEnMpnMXjUHWEd9Qn9To9C/1Cf0NzUK/Ut9Qn9To98SDsEuNX8YrK6udrRfuVxuXM7l\ncnvaJmCN+oT+pkahf6lP6G9qFPqX+oT+pka/JRyCXUqlUpFIJBrXS6VSR/ut/+DJZrN73i5AfUK/\nU6PQv9Qn9Dc1Cv1LfUJ/U6PfEg7BHlj/gVAoFDra5/nz5xGxljSv/0AC9pb6hP6mRqF/qU/ob2oU\n+pf6hP6mRtcIh2APnD9/vnF5ZWVl27kq8/l84/L4+HjP2gWoT+h3ahT6l/qE/qZGoX+pT+hvanSN\ncAg28fXXX3e8bTabjZGRkcb1x48fb7l9/f5MJhOnTp3aWQNhgKlP6G9qFPqX+oT+pkahf6lP6G9q\ntHvCIfj/isXihuudzjdZd/369cbl+fn5TfdfWFhoDEO8efNmd42EAaU+ob+pUehf6hP6mxqF/qU+\nob+p0d37s3feeeedg24EHIR8Ph+///3v4//+7/9iZWUlZmZm4ptvvmncX6lU4ssvv4wvv/wyKpVK\n/P73v4/vf//78corr7R9vNdeey1+9KMfNYYZLiwsxKuvvhqvvfZaJJPJKJfL8ejRo/jP//zPSCaT\ncefOnfjzP//zfTlWOGzUJ/Q3NQr9S31Cf1Oj0L/UJ/Q3Nbr3hl6+fPnyoBsBB+HSpUtd7zM5ORmn\nT5/ecptKpRIzMzOxsrLS9v7R0dG4fv36kVm4DHpBfUJ/U6PQv9Qn9Dc1Cv1LfUJ/U6N7TzgEPVKr\n1aJQKESlUolkMhnDw8ORzWaP5AcJHDbqE/qbGoX+pT6hv6lR6F/qE/rbINaocAgAAAAAAGCAfOeg\nGwAAAAAAAMD+EQ4BAAAAAAAMEOEQAAAAAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ\n4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAAAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ\n4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAAAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ\n4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAAAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ\n4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAAAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAyQ\n7x50AwAAAA5SPp+PTCYTqVTqoJvCHpmbm4uFhYWoVCoxMjIS2Ww2JiYmDrpZA6NcLsfQ0JCaAgDo\nY0MvX758edCNAAAA2A/VajVWV1ejVCpFoVCIZ8+eRUTExMREXLhw4YBbx24Vi8WYmpqKWq3Wct/H\nH398AC0aDPW6KhQKsby8HJVKJbLZbLz99tsH3TQAADZh5BAAAHDkzczMxNLSUtvQgKOhXC7HW2+9\nddDNGBj5fD6ePHkSxWKxbV0NDQ0dQKsAAOiUcAgAADjykslk/MVf/EVErIUI5XL5gFvEXqpWq3Hr\n1q1IJpMxNjYWJ06ciIiIzz77LObn5w+4dUfTixcvolKpxLFjx+LYsWNqCgDgkBEOAQAAR17zejOX\nLl06oJaw1+rBUK1Wi9u3b8epU6ca950+fTouXrwYCwsLB9jCo2lsbCzGxsYa12/duhWlUukAWwQA\nQDe+c9ANAAAA2G8jIyMH3QT2yPT0dFQqlUgmkxuCobpEImE9qX2QzWYPhwtXKAAAFLlJREFUugkA\nAHRBOAQAAAycZDJ50E1gD8zNzcWzZ88iQjhx0F6+fHnQTQAAoAvCIQAAAA6lTz75pHF5eHj4AFsC\nAACHi3AIAACAQ6dQKEStVmtcNxoMAAA6JxwCAADg0CmVShuuGzkEAACdEw4BAABw6JTL5YNuAgAA\nHFrCIQAAAA6dFy9eHHQTAADg0BIOAQAAcOhUq9WDbgIAABxa3z3oBgAAwFFXLpcjn8/H06dPI5fL\nxcTExIb7FxYW4smTJ1GpVKJWq0UikYh0Oh2ZTCbOnz8fIyMju3r+QqEQuVxu3/YvFAqRz+djaWkp\nfvOb30Qikdhw39zcXFQqlahUKo1jzWazcfHixQ3btnvcdvuePXs2xsbGttx3J4rFYiwsLMTKykq8\nePGi8dqcOHEiRkdHY2xsbNfPUT9Xq6urjdc/lUpFOp3e8XMUi8V4+vRpLC8vx8TERIyOjjbuK5fL\nMTc3F0tLS43nat5mLxUKhSgUCrGystI4vohoHGMul4vR0dFIpVI9ef69dtC1HHEw5zSfzzeOq157\nJ06ciJGRkTh//vyev369rIt2561+PLlcLnK53J5/lgAA9KOhly9fvjzoRgAAwFFSLBYb/1ZXV+P5\n8+eN+8bGxuLq1asRsdbRfOfOnahUKls+3unTp2NycrKrNtQ7sJeXlyMi4uHDhx13eFar1VhZWYkn\nT57Es2fPIiLi448/3nTb1dXVKJVKsbq6GisrK41O1+bnnZqaajzeZq5evdq247eTfW/evNlxyNH8\neBMTE3HhwoWIWHtdpqenN7xu7SQSibh+/fqOgpWFhYVG0BURkclkIpVKRaVSiWKxuOE5JiYmtuwM\nX/9+q3d8101OTsbp06cjYu09cf/+/Zb9x8fH4/Lly10fw1by+XzMzs62HF+tVotyudyyXtDp06fj\nZz/7WcchQ7vaGRkZiUwm07JtMplsCXE61Q+1XNfrc9pO8/t0M/XQq1QqNW7L5XLxi1/8YlfPt5u6\nqCsWi/HgwYPGa5dOpyOdTkcikYhSqdR27aoLFy7s+D0DAHBYGDkEAAB7aHp6uhHIbKVYLMbU1NSG\nIGUzy8vLcevWrbh79+6m28zNzTVCmu06cpvVarX47W9/29i30/2r1Wr8/Oc/72i7d999d9uwJSLi\nww8/jIhodPpWq9W4detWR226f/9+VwFRO928LrVaLe7fv78hWNpOc/B07dq1OHfuXMvjTk9PN4K2\nDz/8MIrFYly7dq3l8W7cuNHRuSkUCm2DoYiI4eHhjtreiebXemxsLCYmJlqCyVKpFA8ePGiECcvL\ny7G8vLxpOFhXHz3WLigslUobwom6+uiobh1ULTfr9TltZ30QlU6nY3x8PNLpdAwPD8dnn30W5XJ5\nQxDc7rx3Y6/rYv1x1N/3uVwurl692jYsW1hYiNnZ2cbxDA0N7ep4AAAOgz9755133jnoRgAAwFEx\nPDwcJ0+ejL/+67+O7373u/H5559vuH9kZCS++eabuHPnTnzve9+Ln/70p/HGG2/ElStXYmxsLE6e\nPBkvX75s2e+rr76KV199Nf7yL/+y5Tmr1WrcuXMnPv/8803XYXnjjTfilVdeaXvfH//4x/j3f//3\nqFQqm+7/j//4jy23fe9734vXXnst/uZv/iZOnjwZ5XK5Zf8zZ87E1NRUfP7555HJZOLKlStx+fLl\n+OlPfxrZbDa+/PLLlnDjd7/7Xbz++uvxxz/+MSYnJ+Orr75qTOG13b75fD5ef/31TY+17n/+5382\n7JvL5eKVV16Jt956K7755pvI5XIxPj4ef//3fx+vv/56ZLPZtq9LRMTKykr86Ec/ih/+8IdbPmc+\nn49f/epX8dVXX0UymYzp6enIZrMt273yyivx4x//OP73f/83vvrqq4hY63xv9/p///vfj1wuFydP\nnoyIaDkfZ86ciT/96U/xq1/9atN2nTlzpu2Im25Vq9X4p3/6p/jDH/4QEWsd/P/wD//Q9rV47bXX\nYmxsLL788ssNwcLvfve7+NOf/tR2GsNyuRz/9m//1nX4eeLEifjxj3/c5dEcTC036/U5bVYul2Nq\nair+67/+KyLWwserV69GNpuNTCYTP/zhDyOXy8WZM2fijTfeiFdffTVWV1fjm2++aXmsdDrd0Xnv\nRV3Uj6X+vk+n0/H+++9HMpls24ZMJhNnz56N//7v/45vvvkmTp482bYNAABHiWnlAACgh5pHdiQS\niajVapv++r+u3RRgiUQiHj58uOXz1Wq1+Oijj2JxcXHD7Z1OK1er1eI//uM/WkZMbDat3HorKytx\n586dljYPDQ3F5ORknDp1qu1+MzMzLe09fvx4PH/+PJLJZNf7djKSp3laudOnT8fy8nJkMpm4efPm\nplNxVSqVmJqaagkotnttisVivPXWW43rt2/f3vSY6kqlUty6dWvDbR988MGW04RdunRpw/WJiYl4\n/PhxDA0NxdjYWORyuXj58mU8efKk8Rqvn3puN/71X/+1MfJjdHQ0bt682dF+7UbodDIC7M6dO7Gy\nstK43s0Irp3Y71qO2N9zWigU4r333ouItan47t692/GUdO2er5Np5XpZF+vb1OnUifXXqtfvJQCA\nfvCdg24AAAAcZc0L0Nc7k69evbplWDM6OtrSYV+r1badvimRSMT58+d33N7d7N+uI3loaCh++ctf\nbtnh225aqHowdPfu3a73ffr0aYct/tby8nLkcrl4//33t+wQT6VSce/evZZtarVazM/Pt92nWq3G\n1NRU43oul9u2Azxi7b3T/DyffPLJlvs0v6dmZ2fj2LFjcffu3bh8+XKcOnUqstlsTE5ONjq/0+n0\ntm3ZzkcffbRh2sBupnF78803W9p9//79badp2+/fOe53Le/nOa1Wq41gKCLi+vXrXa1VNDk52fVI\nm17XxfqwqtP3eP0YNhthBABwlAiHAABgH6XT6cYi9tt58803W25bXV3ddr/dLEC/m/3brV0zMTER\nx48f33bfdr/Sn5iY6KgtzfvuZP2TZDLZ8aiMRCLRVSj1+PHjDZ3y3ayJ1Lzt0tLSltu3ew2uXbvW\n9jxOTExsel83qtVqfPrpp43rmUymq8dMJBJx8eLFltsfP368q3b1Wi9reb/P6fT0dONyOp3e0Uiy\nTuq8uS29qovm6S2/+OKLjh43mUzGyMjIngSmAAD9TjgEAAD7qJtf1ycSiZYO4W7XWzlonR7viRMn\n9nTfbp05c6ajaffqstlsy0iSUqnUdmRG87R33bwHmjupa7VaV++BTCaz5WiMc+fOdXXc7TQHDjtZ\nq2VsbKzltoWFhR23aT/0spb385wWi8UNUyx2E9Lsxn7WxcLCQsd1s91oRQCAo0I4BAAAfay5E7T5\nF/H9rt1Ilnba/VJ/N/tuNyVZJ4+xnbNnz7bc9uLFiw3XC4VCS1s6Pa7Ntu3m2HYSKnSrea2ZnYR1\niUSipa2dTL12mHRTy/t5TpsDo1wu1/VzdavXddE8LVytVosbN27E7Oxs158NAABHlXAIAAD62G6n\n/DosdnOcB3WOmkcORbSOBmkXbqyurkaxWIxyuRzVanXLkKDdsTUHUFvp9fRY1Wq15Zh3ul5Lu1Ci\nk2kUD4tO36f7fU6bp2Tbj3raj7o4d+5cyzbz8/Nx5cqVuHHjRszMzEQ+nxcWAQAD67sH3QAAAIDD\nqF0HdfOIhq+//rplmzt37uzqeXc7wmIvlcvlPXvOdmHbIHbc7/c53c0Inp3aj7r42c9+FktLS23f\nQ5VKJRYXFxtT26VSqchms5HL5fZtWj0AgIMmHAIAANiBdp3onawr003n8/DwcCSTyUin05FOp2N4\neDiOHz/edVt7pd3xdTOyab3djpI6KvbznLYLovbDftRFIpGIe/fuxfT09LbTE64PixKJRFy/fl1I\nBAAcecIhAACAPZJIJDZcbzc11s2bN/erOT3XLrTY6bpY7cK2/RjF0m/285y2e9wXL160vI/32n7V\nRSqVirt378bKykosLS1tOpJovVqtFvfv34+bN28KiACAI82aQwAAADvQ3InfriO53Vox7UZNHFbt\nwod2U4bt1E7X2jnM9vOctju/xWJxz56rkzbU9bIustlsXLt2LR4+fBhvv/12XLhwoe2Ue+s9ePCg\nZ+0BAOgHRg4BAADsQPNUVWfPnm3ZJp1Ot9x2lNbRaXd8Ow0X2o2YOXHixI4e6zDbz3PaLohaXV3d\n0YiZbt7XB1kX2Ww2stls43p9VFF9/aH17SmVStuGSAAAh5WRQwAAMAC6WbNkp1NYDZpCodC4nE6n\n4/Tp0y3btAs31u932LXrOF9ZWdnRYzW/75LJZF+tr7Rf9vOcthvBk8/nd/Rc3XzG9FNd1EcVffDB\nBy3T6e3HKCoAgIMiHAIAgCOmXYdvN7/K32lH9KBZP9JgYmKi7TaZTKbltsePH/esTQehOcyoVCo7\nmiKsuSN+bGxsV+06zPbznK4fRVN/ruZRcZ1YXl7ueNv9qItLly7F7Oxsx9unUqm4ePHihtsGcc0r\nAGBwCIcAAGAAfPHFFx1vu7Cw0MOW9IduRjm089FHHzUu53K5tqOGItY6nFOp1IbbarXajkdn9KN2\n0+k9efKk68dZWlracP2NN97YcZsOu/08p+2mkOt2vZ319dCJ/aqLoaGhrrZvHqnWbvo7AICjQjgE\nAABHUPP0SE+fPu1ov+np6Z4uDN+vugnPisVifPrppxGxFgz94he/2HL78fHxltsePHhwZM7zhQsX\nWt5v8/PzXR1ftVqNZ8+eNa5PTEy0POYg2c9zOjY21nJ7qVTqeNRNPp9v1EM39qMuyuVyV9uvf+50\nOj2Q0xoCAINDOAQAAEdQ8y/el5eXt50qamZmJpaXl9t22u5UpyN02m2329E9W2meZm9xcTFmZ2e3\nnX6vUCjE1NRURKx1bm8XDEWsdb63GyUxNTXV8fRd5XI5ZmZmYn5+vqPt63p5Dte7fv16y23T09Md\n778+iBgdHY0LFy5su89+HdtB2c9z2m5axPn5+W0Dorm5ubh//35kMplNp1bcTC/ror7WUrdTZK4f\nadXt8QAAHDbCIQAA6KHmTs7V1dVdPV6n+zevIxIRcevWrZidnY2VlZVGCFIul2Nubi6uXLkSi4uL\ncfv27Thz5kzLvp2sWdSubZ3+cr/ddt3+6n+7tqzXLliYn5+PK1euxPT0dOTz+cYogmq1GoVCIaan\np+O9996LY8eOxb179+Ly5csdt+f27dstozMqlUrjNdlstES1Wo2ZmZn453/+51hcXIxcLrfl8zQ/\nTjcjonZjdHQ0zp07t+G2UqnUUZiRz+cb6zflcrm4efNmR8/ZfKyfffZZh63dmf2u5f08p2NjY22n\nRpyfn49bt27FwsJC43yXy+XI5/Nx48aNePToUYyOjsb777/f8v7u5L3Xq7qo13e1Wo07d+5s246I\ntanx6iOtNjsfAABHydDLly9fHnQjAADgqFhYWGgsAr+6uhrPnz9v2eb48eNx4sSJiFhbmL3dIvF1\nMzMzjU7e5v0TiUT87d/+bcvi9RFrYc6VK1c6bncymYxf/vKXcfz48SgUCvHee+9tuH9iYqJl5EGt\nVovf/va3jeuLi4stIVIikYixsbHGwu71UUmd7ru+c/wnP/lJ22mxqtVq/PznP99y37/7u7/bMErh\n0qVLjeOujzLoxOjoaMfhRbNKpRJTU1ObdninUqlIp9ONYyyVShu2vXbtWktYMDc3F19//XVErI2S\naPd+O3fu3Ibz1nwu9lK792sqlYpr1661BJbVajUeP37cmJJsfHx8y8Bt/bGWSqUNU6bVnT59unFs\nf/VXf9U2JO1Uv9RyL89ps+np6VheXu54+/WfC3Nzc/Ho0aMN92ez2cbUbD/4wQ/anp9e1EXzZ1gq\nlYrz58+3nUKvUCjE7Oxs4/Xt9pwBABxWwiEAANhDd+7c6Woqo+3ChnYdw+u1C23qSqVSvPvuu9uO\n+slkMnHz5s1Gp3q7cGi9kZGRuHv3bhSLxXjrrbe2fOxmH3/8cUTEjva9d+9e2zVA2oVDzW7fvh2n\nTp1qXL906VJkMpm4fft2lMvlePz48Zad4ul0OiYmJvZkNEEn03XVJZPJOHPmzKbrxdRDrm40n4u9\nls/n2476SCQSkclkIplMRrlcbnTGj46OxsWLF7dd3+XGjRtdrUczNjYWV69e7bb5Df1Uy706p908\nV7PJyckN9TA7O7vltIfpdDp+/etfb3r/XtbFwsJCfPjhh5vun0ql4sWLFxs+G3O5XExMTFhnCAAY\nGMIhAAA44ubn56NQKESxWGyMkkmn0zEyMhLnz59vCQpKpVI8efIkhoaG2j5eMpk89L+sX15ebgl6\narVarK6uRqVS2XCeerUwfT6fj6dPn8Yf/vCHKJfLUa1WI5lMRjqdjkwmE2fOnOlpiNNrKysrsbS0\nFCsrK1GtVhvHNzw8HCMjI5HL5SKbzfZsFNNRtJ/ndGVlJUqlUqyurkatVosXL17E8PBwpFKpGB8f\n79nrtld1UavVolAoRKFQiFKpFNVqtTFV5fpzduLEiRgdHfU+BAAGjnAIAAAAAABggHznoBsAAAAA\nAADA/hEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAA\nAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAA\nAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAA\nAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAA\nAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAA\nAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMEOEQAAAA\nAADAABEOAQAAAAAADBDhEAAAAAAAwAARDgEAAAAAAAwQ4RAAAAAAAMAAEQ4BAAAAAAAMkP8HMZur\nVoLlITcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b4af22b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(dimensions ** 2, convolution_times, 'x')\n",
    "plt.xlabel(r'number of nodes')\n",
    "plt.ylabel(r'execution time per $p$ (s)')\n",
    "plt.title('percolate.percolate.\\_binomial\\_pmf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b4a8c87b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(dimensions ** 2, convolution_mem / 2 ** 10, 'x')\n",
    "plt.xlabel(r'number of nodes')\n",
    "plt.ylabel(r'memory consumption per $p$ (GiB)')\n",
    "plt.title('percolate.percolate.\\_binomial\\_pmf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dAAAg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\nF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b4ad44940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(dimensions ** 2, run_times, 'x')\n",
    "plt.xlabel(r'number of nodes')\n",
    "plt.ylabel(r'execution time (s)')\n",
    "plt.title('percolate.hpc.bond\\_microcanonical\\_statistics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b4a88dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(dimensions ** 2, run_mem / 2 ** 10, 'x')\n",
    "plt.xlabel(r'number of nodes')\n",
    "plt.ylabel(r'memory consumption (GiB)')\n",
    "plt.title('percolate.hpc.bond\\_microcanonical\\_statistics')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
